1. Field of the Invention
This invention relates to the field of Inertiai Navigation Systems (INS) and satellite positioning systems such as the Global Positioning System (GPS). In particular, this invention relates to methods of integrating INS and GPS data in order to provide more accurate navigation solutions.
2. Discussion of Prior Art
An INS comprises a set of accelerometers and gyroscopes, known as an inertial measurement unit (IMU), together with a navigation equations processor, which integrates the IMU outputs to give the position, velocity and attitude. GPS consists of a constellation of satellites which transmit navigation data to a GPS receiver. User location can be derived from the signals received from four separate satellites. Together, INS and GPS form the core navigation systems of military aircraft and missiles. Note: although the term “GPS” is used throughout the skilled man will appreciate that the invention relates to any satellite navigation system that works along similar principles to GPS, e.g. Galileo. References to GPS should therefore be taken as meaning any satellite system that operates in a GPS-like manner.
Integrating INS and GPS together provides a navigation solution which combines the long term accuracy of GPS with the continuity, high bandwidth and low noise of INS.
There are four basic types of INS/GPS integration technique. An uncoupled system simply uses the GPS data to periodically reset the INS. This approach is crude and, hence, is rarely used A loosely-coupled system compares the GPS navigation solution to that of the INS to estimate errors in both systems using a Kalman filter based algorithm (for more information on the Kalman filter algorithm see Applied Optimal Estimation by The Technical Staff of the Analytical Sciences Corporation, editor A Gelb, Massachusetts Institute of Technology Press (1974)). A tightly-coupled system, is similar to the loosely-coupled system but uses the range and range rate data transmitted from each satellite tracked instead of the GPS navigation solution. Finally, a deep integration system combines the GPS receiver tracking functions and INS/GPS integration within a common Kalman filter. This requires the re-design of, amongst other things, the GPS receiver, which requires access to the GPS Receiver Applications Module (GRAM), which is restricted by the US Government. Processor loads in a deep integration system are also high and so this system has a number of drawbacks.
Both loosely-coupled and tightly-coupled systems are in common use. However, tightly coupled systems are more accurate and stable and are the subject of this invention (for further information on GPS/INS integration see GPS/INS Integration, AGARD Lecture Series LS-207; Systems Implications and Innovative Applications of Satellite Navigation by R E Phillips and G T Schmidt).
Each navigation satellite transmits carrier signals on two frequencies, known as L1 and L2, each with a pseudo-random code modulated onto it. The GPS receiver will track the code and carrier components of each signal independently. Each receiver will therefore maintain two so-called tracking loops for each satellite signal. Range data (referred to as “pseudo-range” in GPS terminology) is derived from the code signal tracking loop and range-rate data (referred to as “pseudo-range rate”) is derived from the carrier signal tracking loop. In normal GPS operation, each carrier tracking loop is used to aid the corresponding code tracking loops. However, carrier tracking loops are more sensitive to interference and will lose lock at lower interference levels than code tracking loops. The position of the receiver can be derived from the pseudo-range information and the velocity of the receiver can be derived from the pseudo-range rate information.
The responsiveness of a GPS receiver is affected by noise (e.g. from interference with the GPS signal) and also by high dynamic vehicle manoeuvres. The bandwidth of the tracking loops is a measure of the frequency with which the receiver outputs independent range and range rate measurements. High bandwidths enable a receiver to track the receiver location more quickly whereas low bandwidths provide greater resistance to interference. It is thus important to select bandwidths carefully in order to maintain satisfactory receiver performance.
In the military environment many GPS receivers are capable of adapting their tracking loop bandwidths in order to respond to changes in the level of vehicle motion and interference.
In an integrated INS/GPS (tightly coupled) system the pseudo-range and pseudo-range rate data from the GPS tracking loops are used as measurement inputs to a Kalman filter. In dual frequency receivers, the outputs from the L1 and L2 tracking channels are combined prior to input to the Kalman filter in order to correct for ionosphere propagation delays. A reversionary mode is usually implemented whereby INS data aids the code tracking loop in the event that the carrier tracking loop loses lock and the GPS receiver is unable to derive range-rate data.
The Kahnan filter is an estimation technique which provides an estimate of the GPS/INS system errors. Part of the Kalman filter technique is the calculation of the so-called Kalman gain matrix (Kk) which relates the accuracy of the current measurement to that of the previous estimates of the system errors. In order to correctly calculate the measurement errors in the system the Kalman filter assumes that all measurements have time uncorrelated measurement errors. Gelb defines the Kalman gain matrix (Kk) asKk=Pk(−)HkT[HkPk(−)HkT+Rk]−1where Hk is the measurement matrix, Rk is the measurement noise covariance and [ ]−1 denotes the inverse of the matrix.
In fact the errors in successive pseudo-range and pseudo range-rate data are correlated with correlation times inversely proportional to the tracking loop bandwidths. If this fact is not addressed then the Kalman filter becomes unstable, resulting in degraded estimates. Where the Kalman filter corrected INS data is used to aid GPS code tracking, a form of positive feedback can occur which eventually causes the GPS receiver to lose its tracking locks. The navigation solution cannot be resurrected from the INS data alone, where the corrected INS data is used to aid GPS, either because there is no stand alone INS solution or because the INS solution, if available, is not accurate enough. Therefore, where GPS receivers that do not have adaptive bandwidths are used, this problem is circumvented by ensuring that the Kalman filter updates its estimate of the measurement errors at an interval which is greater than the tracking loop measurement correlation time (of the order of 1 second), i.e. the interval between iterations of the Kalman filter measurement update phase is chosen to be greater than the tracking loop measurement correlation time. Since different receivers use different tracking loop bandwidths it is important that the INS/GPS integration Kalman filter is correctly tuned to the appropriate tracking loop bandwidths.
In cases where the receiver has adaptive tracking loop bandwidths tuning of the integration algorithm becomes more difficult. A common approach is to tune the algorithm to a relatively high bandwidth level and disable the Kalman filter measurement inputs when the tracking loop bandwidth drops below a threshold value. This is obviously not an ideal solution since measurement data is being discarded which will inevitably result in a less than optimum navigation solution.